A typical emission scan using a PET scanner begins with the injection of a solution including a tracer, which is a pharmaceutical compound including a radio-isotope with a short half-life, into the subject. The subject may be human or animal. The tracer moves to, and is typically taken up, in one or more organs in the subject according to biological and biochemical processes which occur within the subject. When the radio-isotope decays, it emits a positron, which travels a short distance before annihilating with an electron. This annihilation produces two high energy photons propagating in opposite directions. The PET scanner includes a photon detector array arranged (usually in a ring-shaped pattern) around the scanning area. If two photons are detected within a short timing window, a so-called “coincidence” is recorded along a line of response (LOR) connecting the two detectors. Coincidence counts along each LOR are listed each time a coincidence is detected between the corresponding detector pair, and subsequently the list is processed by, for each event, incrementing a data storage part referred to as a sinogram bin to build up a sinogram. The output sinogram is typically processed using image reconstruction algorithms to obtain volumetric medical images of the subject.
For quantitative results from PET images, attenuation correction generally forms one of the data correction stages. In a conventional PET scanner, the scanner is provided with one or more positron emitter rod sources, formed of a material such as 68Ge, which emit dual annihilation photons. To derive attenuation factors, two acquisitions using the rod sources are conventionally used—a blank scan, in which the subject being scanned is not present in the scanning area (typically, the scanner is empty except for the presence of the sources) and a transmission scan in which the subject is present in the scanning area. Since the source material is a positron emitter, the two photons arising from the annihilation of a positron and an electron are acquired in coincidence, in the same manner as with an emission scan. Conventionally, the results of the blank scan are then divided by the results of the transmission scan to derive an attenuation sinogram. The attenuation sinogram is then used to correct the emission scan for attenuation.
Typical PET scanners have detector arrangements which do not rotate during an acquisition. Typically, the detectors are arranged in two or more banks of detector rings. Alternatively, the detectors may be arranged in a non-ring-shaped pattern. In any case, there will generally be directions in which coincidences are not detectable due to the geometry of the detector array, since the scanner has a finite field of view and there may be “blind spots” due to gaps between the detectors, etc.
With the increasing resolution of PET scanners, subject movement becomes an important degrading factor in the quality of the data. For example with brain scans, head movement causes a time-varying rigid body transformation of the brain, and hence of the radioactivity distribution to be reconstructed. The head movement can be externally monitored, and the effects of the monitored movements can be corrected. Several methods have been proposed to correct for subject movement. They fall into 3 classes:
(a) Post-processing of the image using deconvolution. See for example the papery by M. Menke, M. S. Atkins, K. R. Buckley, “Compensation Methods for Head Motion Detected During PET Imaging”, IEEE TNS vol. 43, (1996) 310.
(b) Multiple Acquisition Frame (MAF) methods, i.e. splitting the acquisition into short time frames (which may be triggered by a threshold on the amount of motion), followed by reconstruction each short time frame, realignment of the images and then combining all these images into one image. See for example the paper by Y. Pickard, C. J. Thompson, “Motion correction of PET images using multiple acquisition frames”, IEEE TMI vol. 16 (1997) 137.
(c) Acquiring the data in list-mode and realigning the events according to the known motion before binning them into a sinogram, followed by reconstruction of the sinogram. See for instance the paper by P. Bloomfield, T. J. Spinks, J. Reed et al., “The design and implementation of a motion correction scheme for neurological PET”, PMB 48 (2003) 959 for a discussion of the merits of these methods and more complete references, and a paper by S. K. Woo, H. Watabe, et al, “Sinogram-based motion correction of PET Images using Optical Motion Tracking and List-mode Data”, Conf. Proc. IEEE MIC 2002. Both the Bloomfield et al. paper and the Woo et al. paper describe reconstructing the data with a Fourier Rebinning (FORE) algorithm followed by a Filtered Back Projection (FBP) algorithm, and mention that artefacts in the images depending on the type of motion.
A paper by H. Watabe, N. Sato, et al, “Correction of Head Movement Using Optical Motion Tracking System during PET Study with Rhesus Monkey”, “Brain Imaging Using PET” eds. M. Senda, Y. Kimura, P. Herscovitch, Academic Press ISBN 0-12-636651-9 p 1-8 2002 observes that in 2D PET, rotation can move LORs to oblique LORs, and suggested a solution which essentially amounts to a Single Slice Re-Binning (SSRB) approximation.
It is an object of the present invention to provide improvements relating to motion correction for tomography, in particular but not exclusively PET scanners.
In a poster entitled “Correction of Motion in PET using Event-Based Rebinning Method: Pitfall and Solution” by Jinyi Qi and R. H. Huesman of the Centre for Functional Imaging, Lawrence Berkeley National Laboratory, a method of event-by-event motion correction is proposed in which LORs are re-aligned, and some LORs are re-scaled due to the fact that not all LORs are visible to the scanner once a patient has moved. However, some readings along LORs may be so low that resealing would lead to the introduction of significant errors, rather than reducing such errors.
In accordance with one aspect of the present invention there is provided method of conducting motion correction for a tomographic scanner including a detector array for detecting radiation to generate detector data, wherein the method comprises:
storing detector data collected during a data acquisition period, said detector data being indicative of:                i) directions along which radiation is detected; and        ii) quantities of radiation detected in different of said directions;        
storing movement data representing movement of the subject during the data acquisition period; and
motion correcting said detector data using said movement data and a motion correction algorithm to calculate motion corrected detector data,
wherein said motion correcting step comprises processing said detector data by:                a) realigning directions of at least some of said detector data on the basis of said movement data; and        b) altering quantities of at least some of said detector data on the basis of said movement data,        
such that at least some of said detector data are both realigned and altered in quantity,
wherein said altering quantities step comprises calculating estimates of first detector data based on second, different, detector data.
In preferred embodiments of the invention, the altering quantities step comprises both scaling quantities upwards and calculating estimates based on other of said detector data. Preferably, the method comprises selectively either scaling a quantity upwards or replacing a quantity with a calculated estimate. Alternatively, the method may comprise altering the quantity so that the altered quantity takes into account both the original quantity and a separately calculated quantity (for example, by weighted average of a rescaled quantity and a separately calculated estimate.)
Typically, the motion corrected data will then be used as input data for an image reconstruction algorithm to produce a reconstructed image. The present invention provides a new motion correction procedure which can be used to reduce the appearance of artefacts in the reconstructed images due to particular types of motion of the subject, including translation along the scanner axis and rotation about a rotation axis orthogonal to the scanner axis.
Aspects of the invention further include computer software and a scanner system arranged to carry out the method of the invention.
Further features and advantages of the present invention will become apparent from the following description of preferred embodiments of the present invention, made by way of example only with reference to the accompanying drawings.